Abstract
In this work, we introduce an application autotuning framework to dynamically adapt applications in multicore architectures. In particular, the framework exploits design-time knowledge and multi-objective requirements expressed by the user, to drive the autotuning process at the runtime. It also exploits a monitoring infrastructure to get runtime feed-back and to adapt to external changing conditions. The intrusiveness of the autotuning framework in the application (in terms of refactoring and lines of code to be added) has been kept limited, also to minimize the integration cost. To assess the proposed framework, we carried out an experimental campaign to evaluate the overhead, the relevance of the described features and the efficiency of the framework.
Davide Gadioli
Assistant Professor
He earned his M.S. in Information Technology (2013) and Ph.D. cum laude in 2019 from Politecnico di Milano. A former Visiting Student at IBM Research (2015), he is now a postdoctoral researcher at DEIB, focusing on application autotuning, approximate computing, molecular docking, and drug discovery. He contributes to EXSCALATE software development.
Gianluca Palermo
Full Professor
Gianluca Palermo received the M.Sc. degree in Electronic Engineering in 2002, and the Ph.D degree in Computer Engineering in 2006 from Politecnico di Milano. He is currently an associate professor at Department of Electronics and Information Technology in the same University. Previously he was also consultant engineer in the Low Power Design Group of AST – STMicroelectronics working on network on-chip and research assistant at the Advanced Learning and Research Institute (ALaRI) of the Università della Svizzera italiana (Switzerland). His research interests include design methodologies and architectures for embedded and HPC systems, focusing on AutoTuning aspects.